Two-Stage Inference for LncRNA-Disease Associations Based on Diverse Heterogeneous Information Sources

Zhang, Yi; Chen, Min; Xie, Xiaolan; Shen, Xianhao; Wang, Yu

doi:10.1109/access.2021.3053030

Cited by 4 publications

(3 citation statements)

References 55 publications

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“…Considering that lncRNAs with similar functions are often associated with similar diseases, we calculate the functional similarity between diseases based on the semantic similarity of diseases. This type of method is used in many lncRNA-disease associations (Chen et al, 2020;Zhang et al, 2020;Zhang et al, 2021). It will not be introduced in detail here.…”

Section: Lncrna Functional Similaritymentioning

confidence: 99%

Inferring Latent Disease-lncRNA Associations by Label-Propagation Algorithm and Random Projection on a Heterogeneous Network

Chen

Deng

et al. 2022

Front. Genet.

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Long noncoding RNA (lncRNA), a type of more than 200 nucleotides non-coding RNA, is related to various complex diseases. To precisely identify the potential lncRNA–disease association is important to understand the disease pathogenesis, to develop new drugs, and to design individualized diagnosis and treatment methods for different human diseases. Compared with the complexity and high cost of biological experiments, computational methods can quickly and effectively predict potential lncRNA–disease associations. Thus, it is a promising avenue to develop computational methods for lncRNA-disease prediction. However, owing to the low prediction accuracy ofstate of the art methods, it is vastly challenging to accurately and effectively identify lncRNA-disease at present. This article proposed an integrated method called LPARP, which is based on label-propagation algorithm and random projection to address the issue. Specifically, the label-propagation algorithm is initially used to obtain the estimated scores of lncRNA–disease associations, and then random projections are used to accurately predict disease-related lncRNAs.The empirical experiments showed that LAPRP achieved good prediction on three golddatasets, which is superior to existing state-of-the-art prediction methods. It can also be used to predict isolated diseases and new lncRNAs. Case studies of bladder cancer, esophageal squamous-cell carcinoma, and colorectal cancer further prove the reliability of the method. The proposed LPARP algorithm can predict the potential lncRNA–disease interactions stably and effectively with fewer data. LPARP can be used as an effective and reliable tool for biomedical research.

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Section: Lncrna Functional Similaritymentioning

confidence: 99%

Inferring Latent Disease-lncRNA Associations by Label-Propagation Algorithm and Random Projection on a Heterogeneous Network

Chen

Deng

et al. 2022

Front. Genet.

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“…In addition, several lncRNA-disease association prediction methods [60][61][62][63][64], drug-disease association prediction method [65], and several related computational methods [66][67][68][69] can provide help in the prediction of miRNA-disease associations. In this paper, a new method, network projection-based dual random walk with restart (NPRWR), which integrates dual random walk with restart and network projection technology, is proposed to predict potential miRNA-disease associations.…”

Section: Introductionmentioning

confidence: 99%

A novel miRNA-disease association prediction model using dual random walk with restart and space projection federated method

Deng

Yan

et al. 2021

PLoS ONE

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A large number of studies have shown that the variation and disorder of miRNAs are important causes of diseases. The recognition of disease-related miRNAs has become an important topic in the field of biological research. However, the identification of disease-related miRNAs by biological experiments is expensive and time consuming. Thus, computational prediction models that predict disease-related miRNAs must be developed. A novel network projection-based dual random walk with restart (NPRWR) was used to predict potential disease-related miRNAs. The NPRWR model aims to estimate and accurately predict miRNA–disease associations by using dual random walk with restart and network projection technology, respectively. The leave-one-out cross validation (LOOCV) was adopted to evaluate the prediction performance of NPRWR. The results show that the area under the receiver operating characteristic curve(AUC) of NPRWR was 0.9029, which is superior to that of other advanced miRNA–disease associated prediction methods. In addition, lung and kidney neoplasms were selected to present a case study. Among the first 50 miRNAs predicted, 50 and 49 miRNAs have been proven by in databases or relevant literature. Moreover, NPRWR can be used to predict isolated diseases and new miRNAs. LOOCV and the case study achieved good prediction results. Thus, NPRWR will become an effective and accurate disease–miRNA association prediction model.

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“…A method of internal confidence-based local radial basis biological network (ICLRBBN) is proposed by . A two-stage prediction model (DRW-BNSP) is proposed by Zhang et al (2021). HAUBRW algorithm is proposed by Xie et al (2020b), which combines thermal diffusion algorithm and probabilistic diffusion algorithm to redistribute resources.…”

Section: Introductionmentioning

confidence: 99%

GBDTLRL2D Predicts LncRNA–Disease Associations Using MetaGraph2Vec and K-Means Based on Heterogeneous Network

Duan

Kuang

Wang

et al. 2021

Front. Cell Dev. Biol.

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In recent years, the long noncoding RNA (lncRNA) has been shown to be involved in many disease processes. The prediction of the lncRNA–disease association is helpful to clarify the mechanism of disease occurrence and bring some new methods of disease prevention and treatment. The current methods for predicting the potential lncRNA–disease association seldom consider the heterogeneous networks with complex node paths, and these methods have the problem of unbalanced positive and negative samples. To solve this problem, a method based on the Gradient Boosting Decision Tree (GBDT) and logistic regression (LR) to predict the lncRNA–disease association (GBDTLRL2D) is proposed in this paper. MetaGraph2Vec is used for feature learning, and negative sample sets are selected by using K-means clustering. The innovation of the GBDTLRL2D is that the clustering algorithm is used to select a representative negative sample set, and the use of MetaGraph2Vec can better retain the semantic and structural features in heterogeneous networks. The average area under the receiver operating characteristic curve (AUC) values of GBDTLRL2D obtained on the three datasets are 0.98, 0.98, and 0.96 in 10-fold cross-validation.

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Two-Stage Inference for LncRNA-Disease Associations Based on Diverse Heterogeneous Information Sources

Cited by 4 publications

References 55 publications

Inferring Latent Disease-lncRNA Associations by Label-Propagation Algorithm and Random Projection on a Heterogeneous Network

Inferring Latent Disease-lncRNA Associations by Label-Propagation Algorithm and Random Projection on a Heterogeneous Network

A novel miRNA-disease association prediction model using dual random walk with restart and space projection federated method

GBDTLRL2D Predicts LncRNA–Disease Associations Using MetaGraph2Vec and K-Means Based on Heterogeneous Network

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